Table REF  presents our results on LVIS. In the R50 comparison, F-VLM ranks second among the other alternatives based on knowledge distillation, pretraining, or joint training with weak supervision. The leading DetPro [1]} adds prompt optimization [2]} and SoCo pretraining [3]} upon ViLD [4]}, all of which are orthogonal to our approach. In the system-level comparison, we observe the performance of F-VLM scales up nicely with frozen model capacity, even though the amount of trainable parameters is fixed. Our best model achieves 32.8 AP\(_r\) , which is +14.2 AP\(_r\)  from the R50 baseline and the best published results on this benchmark to our knowledge. Compared to the best existing approach (ViLD-EN-B7), we outperform by 6.5 mask AP\(_r\)  on the novel categories (and +5.6 overall mask AP). Notably, F-VLM is the only approach without a trainable backbone.
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